Technology juggernauts–despite their larger staffs and budgets–still face the “cognitive load” for DevOps that many organizations deal with day-to-day. That’s what led Spotify to build Backstage, which supports DevOps and platform engineering practices for the creation of developer portals.

As you know, having reliable checks is a cornerstone of synthetic monitoring. We don’t want false alarms, or worse, checks succeeding when things aren’t working. But sometimes, problems can be hard to identify because they only happen intermittently, or in certain situations. Similarly, monitoring results can be skewed by infrastructure issues, or network errors on the monitoring provider end, causing false alarms when there is actually no problem with the product.

The panel discussion “From Machine Data to Business Insights, Building the Foundations of Industrial Analytics” discussed modern methods and benefits of deriving insights from machine data. InfluxDB Developer Advocate Jay Clifford explained the trend now is to “allow the builders to bring the Lego blocks and build them together how they see fit.

Following our well-received presentations with Walgreens’ Andy Kettlewell at this year’s NRF and RILA LINK shows, we were fortunate to present another testimonial breakout session—this time from a CPG perspective—at Gartner’s annual Supply Chain Symposium/Xpo in Orlando. One of the things I’m especially proud of since the beginning of antuit.ai is the number of customers who’ve been eager to come forward and share their positive experiences with us.

In a previous blog post, we built a small Python application that queries Elasticsearch using a mix of vector search and BM25 to help find the most relevant results in a proprietary data set. The top hit is then passed to OpenAI, which answers the question for us. In this blog, we will instrument a Python application that uses OpenAI and analyze its performance, as well as the cost to run the application.

At Traceloop, we’re solving the single thing engineers hate most: writing tests for their code. More specifically, writing tests for complex systems with lots of side effects, such as this imaginary one, which is still a lot simpler than most architectures I’ve seen: As you can see, when an API call is made to a service, there are a lot of things happening asynchronously in the backend; some are even conditional.